Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for use with a data integration or other computing environment comprising: providing, at a computer including a processor, a design-time system that includes: a graphical user interface for creation of a data flow associated with a software application, including a specification of input hubs and output hubs, wherein each hub operates as one of a data source or target that comprises datasets or entities having attributes, semantics, and relationships with other datasets or entities; and a data source operating as a knowledge source that stores metadata associated with processing the data flow associated with the software application; wherein the software application represents a data flow transformation associated with one or more of the datasets or entities; receiving, via an interface, one or more definitions of additional metadata for use in processing data; processing the metadata received via the interface, to identify information associated with the received metadata, including one or more of a classification, semantic action, template defining a pattern, or service defined by the received metadata; storing the metadata received via the interface, in the data source operating as the knowledge source, to update the knowledge source to include the information associated with the received metadata and to extend functional capabilities provided by the system, including one or more supported types, semantic actions, templates, or services; and identifying a pattern for providing a recommendation for the data flow associated with the software application, based on the information updated in the knowledge source via the interface.
2. The method of claim 1 , further comprising determining a recommendation for a transformation, for use in a dataflow application, wherein the recommendation is determined on a template identified in the data source operating as a knowledge source and provided as a system hub.
This invention relates to dataflow applications, specifically improving the efficiency of data transformations by leveraging a centralized knowledge source. The problem addressed is the lack of automated guidance for selecting optimal data transformations in complex data processing workflows, leading to inefficiencies and errors. The method involves analyzing a data source that operates as a knowledge repository, storing templates or patterns of previously successful transformations. These templates are accessed through a system hub, which acts as a central point for managing and retrieving transformation recommendations. The system identifies relevant templates based on the current data context and suggests transformations that align with proven approaches. This ensures consistency, reduces manual effort, and improves the reliability of data processing pipelines. The method may also include additional steps such as validating the recommended transformation against the data source to ensure compatibility and performance. The system hub may further facilitate collaboration by allowing users to contribute new templates or refine existing ones, continuously enhancing the knowledge base. By integrating this recommendation system into dataflow applications, the invention streamlines workflow design and minimizes the need for trial-and-error experimentation.
3. The method of claim 2 , further comprising performing the transformation on the data flow.
A system and method for processing data flows involves transforming data as it moves through a network or processing pipeline. The method addresses the challenge of efficiently modifying data in transit to meet specific requirements, such as formatting, encryption, or protocol conversion, without disrupting the flow. The transformation is applied dynamically to the data stream, ensuring real-time adjustments while maintaining data integrity and minimizing latency. This approach is particularly useful in applications where data must be adapted on-the-fly, such as in cloud computing, IoT networks, or real-time analytics. The transformation may include operations like encryption, compression, or protocol translation, depending on the system's needs. The method ensures that the transformed data retains its original structure and meaning while meeting the target specifications. This solution improves efficiency by reducing the need for separate processing steps and enhances security by applying transformations at the data flow level. The system can be integrated into existing infrastructure, making it adaptable to various environments. The transformation process is optimized to handle high-throughput data streams without significant performance degradation, ensuring seamless operation in demanding applications.
4. The method of claim 1 , wherein the knowledge source is a system hub.
A system hub is used as a knowledge source in a method for managing and retrieving information. The system hub acts as a central repository that integrates multiple data sources, enabling efficient storage, organization, and retrieval of knowledge. It consolidates information from various inputs, such as databases, user queries, or external systems, into a unified structure. The hub may also include processing capabilities to analyze, categorize, or prioritize the data before making it accessible to users or other components of the system. By serving as a knowledge source, the system hub ensures that information is consistently available, up-to-date, and easily accessible, improving decision-making and operational efficiency. The method leverages the hub's centralized architecture to streamline data management, reduce redundancy, and enhance collaboration across different applications or users. This approach is particularly useful in environments where multiple data sources must be harmonized, such as enterprise systems, research platforms, or knowledge management tools. The system hub may also support real-time updates, ensuring that the most current information is always available.
5. The method of claim 4 , wherein metadata received through the interface is stored in the system hub to be accessed by the system for processing a data flow.
A system and method for managing and processing data flows involves a centralized hub that receives and stores metadata through an interface. This metadata is then accessed by the system to facilitate the processing of data flows. The system includes a hub that acts as a central repository for metadata, ensuring that relevant data attributes and configurations are available for use in subsequent processing steps. The interface allows external systems or users to submit metadata, which is then stored in the hub for retrieval and application during data flow operations. This approach enables efficient metadata management, ensuring that data processing tasks can be performed with the necessary contextual information. The system may also include components for validating, transforming, or routing data flows based on the stored metadata, enhancing the flexibility and accuracy of data processing operations. By centralizing metadata storage and access, the system improves consistency and reduces redundancy in data handling processes.
6. The method of claim 5 , wherein the metadata is used by the system to determine semantic actions permitted for the types of data provided through the interface.
This invention relates to a system for managing data access and operations based on semantic metadata. The problem addressed is the need to control and automate data interactions in a way that aligns with the meaning and context of the data, rather than relying solely on rigid access permissions or manual user input. The system processes metadata associated with data elements to determine permissible semantic actions. These actions represent meaningful operations that can be performed on the data, such as retrieval, modification, or aggregation, based on the data's inherent characteristics. The metadata may include information about data types, relationships, or contextual usage, enabling the system to dynamically assess which actions are valid for a given dataset. The system provides an interface through which users or applications interact with the data. The interface is configured to present or restrict available actions based on the metadata-driven analysis. For example, if the metadata indicates that a dataset contains sensitive financial information, the system may permit only read-only actions or require additional authentication before allowing modifications. The metadata may also define relationships between different data types, allowing the system to enforce consistency or integrity rules across multiple datasets. For instance, if one dataset depends on another, the system may restrict actions that could lead to inconsistencies, such as deleting a referenced record without updating dependent records. By leveraging semantic metadata, the system enables more intelligent and context-aware data management, reducing the risk of errors or unauthorized actions while improving usability and automation.
7. The method of claim 1 , wherein the method is performed in a cloud or cloud-based computing environment.
This invention relates to a method for performing computational tasks in a cloud or cloud-based computing environment. The method addresses the challenge of efficiently managing and executing computational workloads in distributed, scalable cloud systems. The method involves distributing tasks across multiple cloud-based resources, optimizing resource allocation, and ensuring efficient processing of data. It includes steps for receiving a computational request, analyzing the request to determine resource requirements, selecting appropriate cloud-based resources, executing the tasks on those resources, and monitoring performance to ensure optimal operation. The method may also involve load balancing, fault tolerance mechanisms, and dynamic scaling of resources based on demand. By operating in a cloud environment, the method leverages the flexibility, scalability, and cost-efficiency of cloud computing to handle varying workloads effectively. The invention aims to improve computational efficiency, reduce latency, and enhance resource utilization in cloud-based systems.
8. A system for receiving third-party definitions for use with a data integration or other computing environment, comprising: one or more processors operable to: provide a design-time system that includes: a graphical user interface for creation of a data flow associated with a software application, including a specification of input hubs and output hubs, wherein each hub operates as one of a data source or target that comprises datasets or entities having attributes, semantics, and relationships with other datasets or entities; and a data source operating as a knowledge source that stores metadata associated with processing the data flow associated with the software application; wherein the software application represents a data flow transformation associated with one or more of the datasets or entities; receive, via an interface, one or more definitions of additional metadata for use in processing data; process the metadata received via the interface, to identify information associated with the received metadata, including one or more of a classification, semantic action, template defining a pattern, or service defined by the received metadata; store the metadata received via the interface, in the data source operating as the knowledge source, to update the knowledge source to include the information associated with the received metadata and to extend functional capabilities provided by the system, including one or more supported types, semantic actions, templates, or services; and identify a pattern for providing a recommendation for the data flow associated with the software application, based on the information updated in the knowledge source via the interface.
This invention relates to a system for enhancing data integration environments by incorporating third-party metadata definitions to improve data processing capabilities. The system addresses the challenge of extending the functionality of data integration tools by allowing users to define and integrate custom metadata, such as classifications, semantic actions, templates, and services, into a centralized knowledge source. This enables the system to dynamically adapt and recommend optimized data flow transformations for software applications. The system includes a design-time environment with a graphical user interface for creating data flows, where users specify input and output hubs representing data sources or targets. These hubs contain datasets or entities with attributes, semantics, and relationships. A knowledge source stores metadata that governs the processing of these data flows. The system receives additional metadata definitions from third parties via an interface, processes them to extract relevant information, and stores them in the knowledge source. This updates the system's capabilities, supporting new data types, semantic actions, templates, or services. The system then identifies patterns in the updated metadata to generate recommendations for improving the data flow associated with the software application. This approach enhances flexibility and scalability in data integration workflows by leveraging external metadata contributions.
9. The system of claim 8 , further comprising determining a recommendation for a transformation, for use in a dataflow application, wherein the recommendation is determined on a template identified in the data source operating as a knowledge source and provided as a system hub.
This invention relates to dataflow applications and addresses the challenge of recommending transformations for data processing tasks. The system identifies and utilizes templates from a data source acting as a knowledge source, which serves as a central hub for the system. These templates are used to generate recommendations for transformations that can be applied within a dataflow application. The system analyzes the data source to extract relevant templates, which may include predefined patterns, rules, or structures for data manipulation. These templates are then processed to determine the most suitable transformation recommendations based on the context of the dataflow application. The system ensures that the recommendations are derived from a centralized knowledge source, enabling consistent and efficient transformation suggestions across different data processing workflows. This approach enhances automation and reduces manual effort in identifying optimal transformations for dataflow applications.
10. The system of claim 9 , further comprising performing the transformation on the data flow.
Technical Summary: This invention relates to data processing systems designed to handle and transform data flows in real-time or near-real-time environments. The system addresses the challenge of efficiently processing and modifying data streams to meet specific requirements, such as formatting, filtering, or aggregation, without disrupting the continuous flow of data. The system includes a data flow processing module that receives and manages incoming data streams from various sources. It also incorporates a transformation module that applies predefined rules or operations to the data flow, altering its structure, content, or format as needed. The transformation may involve filtering out irrelevant data, converting data types, or aggregating information from multiple sources. Additionally, the system ensures that the transformation process is performed seamlessly, maintaining the integrity and continuity of the data flow. This is particularly useful in applications where data must be processed in real-time, such as financial transactions, IoT sensor data, or streaming analytics. The system may also include monitoring and feedback mechanisms to track the performance of the transformation process and adjust parameters dynamically to optimize efficiency and accuracy. This ensures that the data flow remains reliable and meets the desired output specifications. Overall, the invention provides a robust solution for transforming data flows in a controlled and efficient manner, enhancing the usability and value of the processed data.
11. The system of claim 8 , wherein the knowledge source is a system hub.
A system for managing and integrating knowledge sources in a centralized manner to improve data accessibility and decision-making. The system includes a knowledge source, which is a system hub, that collects, organizes, and distributes information from multiple data sources. The hub acts as a central repository, enabling seamless integration of diverse data types, including structured and unstructured data. It processes and standardizes the data to ensure consistency and accuracy, allowing users to access and retrieve information efficiently. The hub also supports real-time updates and dynamic data linking, enhancing the system's responsiveness. Additionally, the system may include a user interface for interacting with the hub, providing tools for data visualization, querying, and analysis. The hub can be configured to prioritize data based on relevance, timeliness, or user preferences, optimizing the retrieval process. This centralized approach reduces redundancy, improves data integrity, and facilitates better decision-making by providing a unified view of the available knowledge. The system is particularly useful in environments where multiple data sources need to be consolidated, such as enterprise systems, research platforms, or collaborative networks.
12. The system of claim 11 , wherein metadata received through the interface is stored in the system hub to be accessed by the system for processing a data flow.
This invention relates to a data processing system designed to manage and utilize metadata for efficient data flow handling. The system includes a central hub that receives and stores metadata through an interface, enabling the system to access and process this metadata when handling data flows. The metadata stored in the hub can include information about data sources, formats, relationships, or other relevant attributes that facilitate data processing tasks. By centralizing metadata in the hub, the system ensures that processing operations can dynamically reference this metadata to optimize data flow management, such as routing, transformation, or validation. The interface allows external systems or users to input metadata, which is then integrated into the hub for system-wide access. This approach enhances data processing efficiency by providing a unified metadata repository that supports consistent and adaptable data handling across different workflows. The system may also include additional components for data ingestion, transformation, or output, all of which can leverage the stored metadata to streamline operations. The invention addresses challenges in managing diverse and dynamic data sources by standardizing metadata access and utilization within a centralized architecture.
13. The system of claim 12 , wherein the metadata is used by the system to determine semantic actions permitted for the types of data provided through the interface.
A system for managing data access and processing includes an interface that receives data inputs from users. The system categorizes the received data into different types based on predefined criteria, such as data format, source, or content characteristics. Metadata associated with the data is analyzed to determine the semantic actions that are permissible for each data type. These actions define how the data can be processed, manipulated, or shared within the system. For example, certain data types may only allow read-only access, while others may permit editing, deletion, or further distribution. The system enforces these restrictions to ensure data integrity and security. The metadata may also include contextual information, such as user permissions, data sensitivity levels, or compliance requirements, which further influence the allowed actions. By dynamically assessing the metadata, the system ensures that users interact with data in a manner consistent with its intended use and regulatory constraints. This approach enhances data governance by automating compliance checks and reducing the risk of unauthorized or inappropriate data handling. The system may also log all actions taken on the data for audit purposes, providing a traceable record of data usage.
14. The system of claim 8 , wherein the system is provided in a cloud or cloud-based computing environment.
A cloud-based system for managing and processing data in a distributed computing environment. The system includes a plurality of computing nodes interconnected via a network, where each node is configured to perform data processing tasks. The system further includes a central controller that coordinates task distribution, monitors node performance, and ensures data consistency across the nodes. The controller dynamically allocates tasks based on node availability, computational capacity, and network latency to optimize resource utilization. The system also includes a data storage layer that distributes data across multiple nodes to enhance redundancy and fault tolerance. The cloud-based deployment allows for scalable and flexible resource allocation, enabling the system to handle varying workloads efficiently. The system may also include security mechanisms such as encryption, access control, and authentication to protect data integrity and confidentiality. The cloud environment provides on-demand access to computing resources, reducing the need for local infrastructure and enabling remote access to the system. The system is designed to support high-availability applications by leveraging cloud redundancy and failover capabilities.
15. A non-transitory computer readable storage medium, including instructions stored thereon which when read and executed by one or more computers cause the one or more computers to perform a method comprising: providing a design-time system that includes: a graphical user interface for creation of a data flow associated with a software application, including a specification of input hubs and output hubs, wherein each hub operates as one of a data source or target that comprises datasets or entities having attributes, semantics, and relationships with other datasets or entities; and a data source operating as a knowledge source that stores metadata associated with processing the data flow associated with the software application; wherein the software application represents a data flow transformation associated with one or more of the datasets or entities; receiving, via an interface, one or more definitions of additional metadata for use in processing data; processing the metadata received via the interface, to identify information associated with the received metadata, including one or more of a classification, semantic action, template defining a pattern, or service defined by the received metadata; storing the metadata received via the interface, in the system hub operating as the knowledge source, to update the knowledge source to include the information associated with the received metadata and to extend functional capabilities provided by the system hub, including one or more supported types, semantic actions, templates, or services; and identifying a pattern for providing a recommendation for the data flow associated with the software application, based on the information updated in the knowledge source via the interface.
This invention relates to a design-time system for creating and managing data flows in software applications. The system provides a graphical user interface for defining data flows, including input and output hubs that act as data sources or targets. Each hub contains datasets or entities with attributes, semantics, and relationships to other datasets or entities. The software application represents a data flow transformation involving these datasets or entities. The system includes a knowledge source that stores metadata associated with processing the data flow. Users can input additional metadata definitions through an interface, which the system processes to extract information such as classifications, semantic actions, templates, or services. This metadata is stored in the knowledge source, updating it and extending its functional capabilities, such as supported data types, semantic actions, templates, or services. The system then identifies patterns in the updated metadata to generate recommendations for optimizing the data flow. The invention enhances data flow design by dynamically incorporating user-defined metadata, improving automation and adaptability in software application development. The knowledge source acts as a central repository, enabling the system to learn from metadata inputs and provide intelligent recommendations for data processing workflows.
16. The non-transitory computer readable storage medium of claim 15 , further comprising determining a recommendation for a transformation, for use in a dataflow application, wherein the recommendation is determined on a template identified in the data source operating as a knowledge source and provided as a system hub.
A system and method for generating recommendations for data transformations in dataflow applications. The technology addresses the challenge of efficiently identifying and applying appropriate data transformations in large-scale data processing environments. The system operates by analyzing a data source that functions as a knowledge source, where templates for data transformations are stored and managed. These templates are used to generate recommendations for specific transformations that can be applied within a dataflow application. The system acts as a central hub, integrating the knowledge source with the dataflow application to streamline the transformation process. By leveraging pre-defined templates, the system reduces the need for manual intervention and ensures consistency in data processing workflows. The recommendations are dynamically generated based on the content and structure of the data source, allowing for adaptive and context-aware transformation suggestions. This approach enhances efficiency, reduces errors, and improves the scalability of dataflow applications by automating the selection of optimal transformation strategies. The system is particularly useful in environments where large volumes of data require consistent and reliable processing, such as in big data analytics, enterprise data integration, and real-time data streaming applications.
17. The transitory computer readable storage medium of claim 16 , further comprising performing the transformation on the data flow.
Technical Summary: This invention relates to data processing systems, specifically methods for handling data flows in computing environments. The problem addressed is the need for efficient and secure transformation of data flows to ensure compatibility, security, or performance optimization during transmission or storage. The invention involves a transitory computer-readable storage medium containing instructions that, when executed, perform a transformation on a data flow. The transformation may include operations such as encryption, decryption, compression, decompression, encoding, decoding, or other modifications to the data. The transformation is applied to the data flow to ensure it meets specific requirements, such as security standards, format compatibility, or bandwidth efficiency. The system may also include additional components, such as a data flow analyzer to assess the characteristics of the incoming data, a transformation engine to apply the necessary modifications, and a validation module to verify the transformed data meets the desired criteria. The transformation process may be dynamic, adapting to changes in the data flow or system requirements in real time. This invention is particularly useful in environments where data must be securely transmitted, such as in cloud computing, network communications, or data storage systems. The transformation ensures that data is processed efficiently while maintaining integrity and security.
18. The non-transitory computer readable storage medium of claim 15 , wherein the knowledge source is a system hub.
A system for managing and integrating knowledge sources in a computing environment addresses the challenge of efficiently organizing and retrieving diverse data from multiple sources. The system includes a knowledge source, which can be a system hub, that collects, processes, and stores information from various inputs. The system hub acts as a central repository, aggregating data from different sources such as databases, APIs, or user inputs, and standardizing it for easy access. The system also includes a knowledge source manager that interacts with the knowledge source to retrieve, update, or modify the stored information. Additionally, a knowledge source interface allows users or other systems to query the knowledge source, enabling seamless integration and retrieval of data. The system may further include a knowledge source processor that performs operations such as filtering, transforming, or analyzing the data to enhance its usability. The system ensures efficient data management by providing a unified interface for accessing and manipulating knowledge from multiple sources, improving data consistency and reducing redundancy. This approach simplifies the process of integrating and utilizing diverse data sets in a computing environment.
19. The non-transitory computer readable storage medium of claim 18 , wherein metadata received through the interface is stored in the system hub to be accessed by the system for processing a data flow.
This invention relates to a system for managing and processing data flows using a centralized hub that stores metadata to facilitate data processing. The system includes a hub configured to receive and store metadata, which describes the structure, origin, or other attributes of data flows. The hub provides an interface for receiving metadata from external sources, such as users or other systems, and makes this metadata accessible to the system for processing data flows. The metadata may include information about data sources, data formats, processing rules, or other relevant details that enable efficient and accurate data handling. By centralizing metadata storage and access, the system ensures consistency and reduces redundancy in data processing operations. The invention improves data management by allowing the system to dynamically adapt to changes in data flows based on the stored metadata, enhancing flexibility and scalability in data processing workflows. The system may be used in various applications, including data integration, analytics, or real-time processing, where metadata-driven processing is essential for maintaining accuracy and efficiency.
20. The non-transitory computer readable storage medium of claim 19 , wherein the metadata is used by the system to determine semantic actions permitted for the types of data provided through the interface.
This invention relates to a system for managing data access and operations through a user interface, focusing on semantic actions based on metadata. The system involves a non-transitory computer-readable storage medium storing instructions that, when executed, enable a computing device to process metadata associated with data types presented in an interface. The metadata defines permissible semantic actions for each data type, ensuring that users can only perform operations that are contextually valid for the data they are interacting with. For example, if the data type is a financial transaction record, the metadata may restrict actions to viewing, editing, or approving, while prohibiting actions like deletion or sharing. The system dynamically enforces these constraints by interpreting the metadata, preventing unauthorized or inappropriate operations. This approach enhances data security and integrity by aligning user actions with predefined rules embedded in the metadata, reducing errors and misuse. The invention is particularly useful in environments where data handling must adhere to strict compliance or operational policies, such as financial systems, healthcare records, or regulated industries. The metadata-driven approach allows for flexible and scalable enforcement of semantic actions without hardcoding rules, making it adaptable to different data types and evolving requirements.
Unknown
September 15, 2020
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